Graph Smoothing for Enhanced Local Geometry Learning in Point Cloud Analysis

2026-01-16

AI Summary

This work addresses the performance degradation in point cloud graph-based methods caused by sparse boundary connections and noise interference. To mitigate these issues, the authors propose a graph smoothing mechanism that alleviates both connectivity sparsity and noise sensitivity. Furthermore, they introduce an enhanced local geometric learning module that integrates an eigenvector-based adaptive geometric descriptor with cylindrical coordinate transformation to more effectively capture local structural information. The proposed approach is seamlessly integrated into a graph neural network framework and demonstrates consistent and significant performance improvements across multiple point cloud understanding tasks—including classification, part segmentation, and semantic segmentation—thereby validating its effectiveness and generalization capability.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Computer Vision: Other Foundations of Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Graph-based methods have proven to be effective in capturing relationships among points for 3D point cloud analysis. However, these methods often suffer from suboptimal graph structures, particularly due to sparse connections at boundary points and noisy connections in junction areas. To address these challenges, we propose a novel method that integrates a graph smoothing module with an enhanced local geometry learning module. Specifically, we identify the limitations of conventional graph structures, particularly in handling boundary points and junction areas. In response, we introduce a graph smoothing module designed to optimize the graph structure and minimize the negative impact of unreliable sparse and noisy connections. Based on the optimized graph structure, we improve the feature extract function with local geometry information. These include shape features derived from adaptive geometric descriptors based on eigenvectors and distribution features obtained through cylindrical coordinate transformation. Experimental results on real-world datasets validate the effectiveness of our method in various point cloud learning tasks, i.e., classification, part segmentation, and semantic segmentation.

Problem

• graph structure
• point cloud analysis
• boundary points
• noisy connections
• local geometry learning

Innovation

• graph smoothing
• local geometry learning
• point cloud analysis
• adaptive geometric descriptors
• cylindrical coordinate transformation